Prediction of Cutting Tool’s Optimal Lifespan Based on the Scalar Indicators and the Wavelet Multi-resolution Analysis

Babouri, Mohamed Khemissi; Ouelaa, Nouredine; Djebala, Abderrazek; Djamaa, Mohamed Cherif; Boucherit, Septi

doi:10.1007/978-3-319-41468-3_24

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…In [27], turning acoustic signals have been decomposed with EMD and then analyzed by Hilbert-Huang Transform, a correlation between the sound pressure amplitude of the IMFs and the tool wear has been found. EMD has been combined with wavelet analysis in [28] in order to identify the different wear modes of a turning carbide tool. In another work [29] a Support vector machine has been fed with energy and energy entropy of IMFs obtained by EEMD and found to be effective for milling tool wear estimation, EEMD is an improved version of the original EMD decomposition proven to give better results in other applications [30 -34].…”

Section: Introductionmentioning

confidence: 99%

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Bouhalais¹,

Nouioua²

2021

Int J Adv Manuf Technol

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Surface finish quality is becoming even more critical in modern manufacturing industry. In machining processes, surface roughness is directly linked to the cutting tool condition, a worn tool generally produces low quality surfaces, incurring additional costs in material and time. Therefore, tool wear monitoring is critical for a cost-effective production line. In this paper, the feasibility of a vibration-based approach for tool wear monitoring has been checked for turning process. AISI 1045 unalloyed carbon steel has been machined with TNMG carbide insert twenty-one times for a total of 27 minutes of machining, which was a necessary amount of time to exceed (300 µm) as a flank wear threshold. Vibration signals have been acquired during the operation and then processed in order to extract a correlation between the surface roughness, tool wear level and vibration comportment. First, Spectral kurtosis has been calculated for the twenty-one performed runs, the signals has then been decomposed with ICEEMDAN, the energy of the high frequency modes has been finally calculated. The Spectral Kurtosis has allowed the locating of the optimal frequency band that contains the machining vibration signature, yet it couldn't have been used for wear monitoring. On the other hand, the energy of optimal frequency ICEEMDAN modes has increased in direct proportion to the increase of surface roughness degradation and thus, to tool wear.

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Section: Introductionmentioning

confidence: 99%

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Bouhalais¹,

Nouioua²

2021

Int J Adv Manuf Technol

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“…Other authors used the intelligent image processing for tool wear monitoring [6], whereas, Wan-Hao Hsieh et al [7] used the vibration of the spindle for the monitoring system. Babouri et al [8] proposed the Wavelet Multi-Resolution Analysis to improve the sensitivity of the vibration scalar indicators for the identification of the wear state during the machining of X200Cr12 steel, they have proven that there is a possibility to associate the tool wear with the vibration generated when machining. In another study [9], they used a spectral indicator named spectral center of gravity (SCG) to highlight the three phases of tool wear, they applied the spectral center of gravity to quantify the changes of the tool natural frequencies amplitudes that result from the evolution of tool wear.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes

Nouioua¹,

Bouhalais²

2021

Preprint

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In machining processes various phenomena occur during cutting operation. These phenomena can disturb the production through the reduction of part quality and accuracy. Therefore, a mastery of this cutting phenomena is needed to define the machining parameters and take full advantage of manufacturing process. An easy way to classify these phenomena is by monitoring incontrollable parameters, such as generated temperature and vibration. The acquired vibration signals can provide information regarding tool life, cutting performances and workpiece defects. This paper evaluates the possibility of monitoring the tool life during the turning process of AISI 1045 steel using Laser Doppler Vibrometer (LDV), the surface roughness has been measured along with the tool-wear until reaching its limit value of 300µm. Furthermore, this paper also outlines the application of CEEMDAN technique to process the acquired signals for the monitoring processes. RMS and SCI indicators have been used to describe the wear progress, then, the artificial neural network has been adopted to achieve a real time wear monitoring. The obtained results qualified the SCI indicator and ANN for online monitoring.

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“…For the processing of the signals recorded, a combination of two out of the four existing vibration analysis domains was used [4][5][6][7][8][9]: the time and time-frequency domain. The timefrequency is based on feature extraction using the wavelet transform (WT) to provide information on localized signals [2,3,10]. It was a good alternative for analyzing the evolution of wear and especially its transient phases.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Tool Wear Evolution during Turning Operation Based on DWT and RMS

Zaida

Bouchelaghem

Chehaidia

2021

DDF

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In cutting process, the wear of the tool remains posed, it describes their progressive failure in regular operation. The tool wear phenomena is mainly caused by abrasion of hard particles, shearing of micro welds between tool and work-material and the exchange of particles between the tool and work material leading to a several forms of tool wear, however, we focused in this study on the frontal wear, also called wear on clearance surface or flank wear. For efficient use of cutting tool according to the technical requirement, the comprehension and the knowledge of the cutting tool wear evolution is necessary. In order to meet this indispensable need, the present paper proposes a two-step tool flank wear monitoring technique based on vibratory signals analysis during the turning operation using a P30 grade metal carbide tool and C45 (XC48) steel. Firstly, discrete wavelet transforms (DWT), has been used to decompose the signal and extract the information, then the scalar indicator Root Mean Square (RMS) value has been used to evaluate the cutting tool stability level. The proposed method offers the possibility to accurately predict break-in tool wear phase, accelerated tool wear phase and the stability period, in which a high quality machining process is guaranteed.

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Prediction of Cutting Tool’s Optimal Lifespan Based on the Scalar Indicators and the Wavelet Multi-resolution Analysis

Cited by 6 publications

References 8 publications

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes

Experimental Study of Tool Wear Evolution during Turning Operation Based on DWT and RMS

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Prediction of Cutting Tool’s Optimal Lifespan Based on the Scalar Indicators and the Wavelet Multi-resolution Analysis

Cited by 6 publications

References 8 publications

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation

Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator &amp; RMS of CEEMDAN modes

Experimental Study of Tool Wear Evolution during Turning Operation Based on DWT and RMS

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Vibration-based tool wear monitoring using Artificial Neural Networks fed by Spectral centroid indicator & RMS of CEEMDAN modes